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of 23
pro vyhledávání: '"Lei, Rayleigh"'
We study a parametric family of latent variable models, namely topic models, equipped with a hierarchical structure among the topic variables. Such models may be viewed as a finite mixture of the latent Dirichlet allocation (LDA) induced distribution
Externí odkaz:
http://arxiv.org/abs/2408.14327
Autor:
Lei, Rayleigh, Rodriguez, Abel
Discrete choice models with non-monotonic response functions are important in many areas of application, especially political sciences and marketing. This paper describes a novel unfolding model for binary data that allows for heavy-tailed shocks to
Externí odkaz:
http://arxiv.org/abs/2407.06395
Autor:
Lei, Rayleigh, Nguyen, XuanLong
We propose models and algorithms for learning about random directions in simplex-valued data. The models are applied to the study of income level proportions and their changes over time in a geostatistical area. There are several notable challenges i
Externí odkaz:
http://arxiv.org/abs/2310.19985
Autor:
Lei, Rayleigh, Rodriguez, Abel
We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative "unfolding" models previously introduced in the literature. We
Externí odkaz:
http://arxiv.org/abs/2308.16288
Autor:
Lei, Rayleigh, Rodriguez, Abel
Latent factor models are widely used in the social and behavioral science as scaling tools to map discrete multivariate outcomes into low dimensional, continuous scales. In political science, dynamic versions of classical factor models have been wide
Externí odkaz:
http://arxiv.org/abs/2305.19380
Autor:
Lei, Rayleigh, Nguyen, XuanLong
We propose models and algorithms for learning about random directions in two-dimensional simplex data, and apply our methods to the study of income level proportions and their changes over time in a geostatistical area. There are several notable chal
Externí odkaz:
http://arxiv.org/abs/2103.12214
Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to navigate in complex traffic scenarios. Motivated by applications arisi
Externí odkaz:
http://arxiv.org/abs/2102.07695
Robust representation learning of temporal dynamic interactions is an important problem in robotic learning in general and automated unsupervised learning in particular. Temporal dynamic interactions can be described by (multiple) geometric trajector
Externí odkaz:
http://arxiv.org/abs/2006.10241
Akademický článek
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Publikováno v:
In Transportation Research Part C September 2022 142
